Federated Learning for Decentralized DDoS Attack Detection in IoT Networks
Yaser Alhasawi, Salem Alghamdi
Abstract
In the ever-expanding domain of Internet of Things (IoT) networks, Distributed Denial of Service (DDoS) attacks represent a significant challenge, compromising the reliability of these systems. Traditional centralized detection methods struggle to cope effectively in the widespread and diverse environment of IoT, leading to the exploration of decentralized approaches. This study introduces a Federated Learning-based approach, named Federated Learning for Decentralized DDoS Attack Detection (FL-DAD), which utilizes Convolutional Neural Networks (CNN) to efficiently identify DDoS attacks at the source. Our approach prioritizes data privacy by processing data locally, thereby avoiding the need for central data collection, while enhancing detection efficiency. Evaluated using the comprehensive CICIDS2017 dataset and compared with conventional centralized detection methods, FL-DAD achieves superior performance, illustrating the potential of federated learning to enhance intrusion detection systems in large-scale IoT networks by balancing data security with analytical effectiveness.